This paper is in the following e-collection/theme issue:

Machine Learning (1400) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (543) eHealth Literacy / Digital Literacy (358) Electronic Health Records (971)

Published on in Vol 6, No 1 (2018): Jan-Mar

Assessing the Readability of Medical Documents: A Ranking Approach

Assessing the Readability of Medical Documents: A Ranking Approach

Assessing the Readability of Medical Documents: A Ranking Approach

Authors of this article:

Jiaping Zheng1 Author Orcid Image ;   Hong Yu1, 2, 3, 4 Author Orcid Image
Article Authors Cited by (15) Tweetations (10) Metrics

Journals

  1. Kirby R, Aggour A, Chen A, Smith C, Theriault C, Matheson K. Manual wheelchair tilt-rest skill: a cross-sectional survey of awareness and capacity among wheelchair users. Disability and Rehabilitation: Assistive Technology 2019;14(6):590 View
  2. Balyan R, Crossley S, Brown W, Karter A, McNamara D, Liu J, Lyles C, Schillinger D, Grabar N. Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study. PLOS ONE 2019;14(2):e0212488 View
  3. Crossley S, Balyan R, Liu J, Karter A, McNamara D, Schillinger D. Developing and Testing Automatic Models of Patient Communicative Health Literacy Using Linguistic Features: Findings from the ECLIPPSE study. Health Communication 2021;36(8):1018 View
  4. Spasic I, Nenadic G. Clinical Text Data in Machine Learning: Systematic Review. JMIR Medical Informatics 2020;8(3):e17984 View
  5. Schillinger D, Balyan R, Crossley S, McNamara D, Liu J, Karter A. Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study. Health Services Research 2021;56(1):132 View
  6. Crossley S, Balyan R, Liu J, Karter A, McNamara D, Schillinger D. Predicting the readability of physicians’ secure messages to improve health communication using novel linguistic features: Findings from the ECLIPPSE study. Journal of Communication in Healthcare 2020;13(4):344 View
  7. Lee D, Grose E, Cross K. Internet-Based Patient Education Materials Regarding Diabetic Foot Ulcers: Readability and Quality Assessment. JMIR Diabetes 2022;7(1):e27221 View
  8. Ji M, Liu Y, Hao T. Predicting Health Material Accessibility: Development of Machine Learning Algorithms. JMIR Medical Informatics 2021;9(9):e29175 View
  9. Gordejeva J, Zowalla R, Pobiruchin M, Wiesner M. Readability of English, German, and Russian Disease-Related Wikipedia Pages: Automated Computational Analysis. Journal of Medical Internet Research 2022;24(5):e36835 View
  10. Wen J, Lei L. Adjectives and adverbs in life sciences across 50 years: implications for emotions and readability in academic texts. Scientometrics 2022;127(8):4731 View
  11. Roscoe R, Balyan R, McNamara D, Banawan M, Schillinger D. Automated strategy feedback can improve the readability of physicians’ electronic communications to simulated patients. International Journal of Human-Computer Studies 2023;176:103059 View
  12. Nattam A, Vithala T, Wu T, Bindhu S, Bond G, Liu H, Thompson A, Wu D. Assessing the Readability of Online Patient Education Materials in Obstetrics and Gynecology Using Traditional Measures: Comparative Analysis and Limitations. Journal of Medical Internet Research 2023;25:e46346 View
  13. Irshad S, Asif N, Ashraf U, Ashraf H. An Analysis of the Readability of Online Sarcoidosis Resources. Cureus 2024 View

Books/Policy Documents

  1. Mondal H, Mondal S, Singla R. Artificial Intelligence in Medical Virology. View
  2. Choi K. Wisdom, Well-Being, Win-Win. View